code: def bottleneck_inception_SE_module(
      inputs, out_channel=256, res=None, scope="inception_block"
  ):
      min_channel = out_channel // 8
      with tf.variable_scope(scope):
          with tf.variable_scope("Branch_0"):
              branch_0 = slim.conv2d(
                  inputs, min_channel * 3, [1, 1], scope="Conv2d_1x1"
              )
          with tf.variable_scope("Branch_1"):
              branch_1 = slim.conv2d(
                  inputs,
                  min_channel * 3 / 2,
                  [1, 1],
                  scope="Conv2d_1x1",
              )
              branch_1 = slim.conv2d(
                  branch_1, min_channel * 3, [3, 3], scope="Conv2d_3x3"
              )
          with tf.variable_scope("Branch_2"):
              branch_2 = slim.conv2d(
                  inputs, min_channel // 3, [1, 1], scope="Conv2d_1x1"
              )
              branch_2 = slim.conv2d(
                  branch_2, min_channel, [3, 3], scope="Conv2d_3x3"
              )
          with tf.variable_scope("Branch_3"):
              branch_3 = slim.max_pool2d(
                  inputs, [3, 3], 1, scope="MaxPool_3x3"
              )
              branch_3 = slim.conv2d(
                  branch_3, min_channel, [1, 1], scope="Conv2d_1x1"
              )
          net = tf.concat(
              axis=3, values=[branch_0, branch_1, branch_2, branch_3]
          )
  
          se_branch = tf.reduce_mean(net, axis=[1, 2])
          se_branch = slim.fully_connected(se_branch, out_channel // 16)
          se_branch = slim.fully_connected(
              se_branch, out_channel, activation_fn=tf.sigmoid
          )
  
          net = net * se_branch[:, None, None, :]
  
          if res:
              inputs = slim.conv2d(
                  inputs, res, (1, 1), scope="bn_res".format(scope)
              )
  
          net += inputs
  
      return net
  
file_path: insightface/reconstruction/ostec/external/landmark_detector/models.py
imports: import tensorflow
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: bottleneck_inception_SE_module(inputs, out_channel, res, scope) and returns: net